ALMOND: Learning an Assembly Language Model for 0-Shot Code Obfuscation Detection Article Swipe
Xuezixiang Li
,
Sheng Yü
,
Heng Yin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3728886
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3728886
Code obfuscation is a technique used to protect software by making it difficult to understand and reverse engineer. However, it can also be exploited for malicious purposes such as code plagiarism or developing malicious programs. Learning-based techniques have achieved great success with the help of supervised learning and labeled training sets. However, when faced with real-life environments involving privately developed and undisclosed obfuscators, these supervised learning methods often raise concerns about generalizability and robustness when facing unseen and unknown classes of obfuscation techniques. This paper presents ALMOND, a novel zero-shot approach for detecting code obfuscation in binary executables. Unlike previous supervised learning methods, ALMOND does not require labeled obfuscated samples for training. Instead, it leverages a language model pre-trained only on unobfuscated assembly code to identify the linguistic deviations introduced by obfuscation. The key innovation is the use of ”error-perplexity” as a detection metric, which focuses on tokens the model fails to predict. Continuous Error Perplexity further enhances this to capture consecutive prediction errors characteristic of obfuscated sequences. Experiments show ALMOND achieves 96.3% accuracy on unseen obfuscation methods, outperforming supervised baselines. On real-world malware samples, it demonstrates an AUC of 0.869 and significantly outperforms the supervise-learning baseline. Our Dataset, pre-trained model, and code of evaluation will be available at https://github.com/palmtreemodel/ALMOND
Related Topics
Concepts
Obfuscation
Computer science
Artificial intelligence
Perplexity
Machine learning
Opcode
Code (set theory)
Source code
Executable
Assembly language
Generalizability theory
Metric (unit)
Supervised learning
Language model
Natural language processing
Software
Programming language
Computer security
Engineering
Set (abstract data type)
Statistics
Operations management
Mathematics
Artificial neural network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3728886
- OA Status
- hybrid
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411523109
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411523109Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3728886Digital Object Identifier
- Title
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ALMOND: Learning an Assembly Language Model for 0-Shot Code Obfuscation DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-06-22Full publication date if available
- Authors
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Xuezixiang Li, Sheng Yü, Heng YinList of authors in order
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https://doi.org/10.1145/3728886Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1145/3728886Direct OA link when available
- Concepts
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Obfuscation, Computer science, Artificial intelligence, Perplexity, Machine learning, Opcode, Code (set theory), Source code, Executable, Assembly language, Generalizability theory, Metric (unit), Supervised learning, Language model, Natural language processing, Software, Programming language, Computer security, Engineering, Set (abstract data type), Statistics, Operations management, Mathematics, Artificial neural networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4311166089, https://openalex.org/W2887787078, https://openalex.org/W2947883149, https://openalex.org/W2926178846, https://openalex.org/W2767094836, https://openalex.org/W3003243900, https://openalex.org/W4401537194, https://openalex.org/W3005641848, https://openalex.org/W2076758681, https://openalex.org/W4281744374, https://openalex.org/W4308632257, https://openalex.org/W4247464060, https://openalex.org/W4234480284, https://openalex.org/W2810872891, https://openalex.org/W4226128225, https://openalex.org/W3133719257, https://openalex.org/W4362615084, https://openalex.org/W2150423842, https://openalex.org/W2901689459, https://openalex.org/W2885072546, https://openalex.org/W2282866165, https://openalex.org/W4401113920, https://openalex.org/W3194813479, https://openalex.org/W4313203541, https://openalex.org/W3089028909, https://openalex.org/W2560204201, https://openalex.org/W2314464932, https://openalex.org/W4391887109, https://openalex.org/W4281385582, https://openalex.org/W2144211451, https://openalex.org/W1565441035, https://openalex.org/W4285271823, https://openalex.org/W2992467173, https://openalex.org/W4285586654, https://openalex.org/W3198685994, https://openalex.org/W2962830343, https://openalex.org/W2525778437, https://openalex.org/W2945413494, https://openalex.org/W2024170198, https://openalex.org/W2997915791, https://openalex.org/W2963804400, https://openalex.org/W3089412163, https://openalex.org/W3129166376, https://openalex.org/W4231934124, https://openalex.org/W1516836232, https://openalex.org/W4398239361 |
| referenced_works_count | 46 |
| abstract_inverted_index.a | 3, 87, 115, 141 |
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| abstract_inverted_index.Our | 197 |
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| abstract_inverted_index.and | 15, 47, 60, 72, 77, 191, 201 |
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| abstract_inverted_index.facing | 75 |
| abstract_inverted_index.making | 10 |
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| abstract_inverted_index.further | 156 |
| abstract_inverted_index.labeled | 48, 107 |
| abstract_inverted_index.malware | 183 |
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| abstract_inverted_index.Dataset, | 198 |
| abstract_inverted_index.However, | 18, 51 |
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| abstract_inverted_index.achieves | 171 |
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| abstract_inverted_index.concerns | 69 |
| abstract_inverted_index.enhances | 157 |
| abstract_inverted_index.identify | 125 |
| abstract_inverted_index.language | 116 |
| abstract_inverted_index.learning | 46, 65, 101 |
| abstract_inverted_index.methods, | 102, 177 |
| abstract_inverted_index.predict. | 152 |
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| abstract_inverted_index.baseline. | 196 |
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| abstract_inverted_index.developed | 59 |
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| abstract_inverted_index.malicious | 25, 33 |
| abstract_inverted_index.privately | 58 |
| abstract_inverted_index.programs. | 34 |
| abstract_inverted_index.real-life | 55 |
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| abstract_inverted_index.training. | 111 |
| abstract_inverted_index.zero-shot | 89 |
| abstract_inverted_index.Continuous | 153 |
| abstract_inverted_index.Perplexity | 155 |
| abstract_inverted_index.baselines. | 180 |
| abstract_inverted_index.developing | 32 |
| abstract_inverted_index.deviations | 128 |
| abstract_inverted_index.evaluation | 204 |
| abstract_inverted_index.innovation | 134 |
| abstract_inverted_index.introduced | 129 |
| abstract_inverted_index.linguistic | 127 |
| abstract_inverted_index.obfuscated | 108, 166 |
| abstract_inverted_index.plagiarism | 30 |
| abstract_inverted_index.prediction | 162 |
| abstract_inverted_index.real-world | 182 |
| abstract_inverted_index.robustness | 73 |
| abstract_inverted_index.sequences. | 167 |
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| abstract_inverted_index.techniques | 36 |
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| abstract_inverted_index.consecutive | 161 |
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| abstract_inverted_index.outperforms | 193 |
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| abstract_inverted_index.undisclosed | 61 |
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| abstract_inverted_index.https://github.com/palmtreemodel/ALMOND | 209 |
| cited_by_percentile_year | |
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| institutions_distinct_count | 3 |
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| citation_normalized_percentile.is_in_top_10_percent | False |